5522 results for "*oma"

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• mRNA-seq of paired Follicular Lymphoma (FL) patient samples before and after high-grade transformation to DLBCL (FL vs. tFL) from FFPE tissues

  The aim of this study was to analyze transcriptional changes in paired samples of follicular lymphoma (FL) and its histological transformation (tFL). Transformation of FL is an adverse event in the course of the disease and is associated with poor outcome. Several genetic aberrations have been linked to tFL pathogenesis, but gene expression changes and microenvironmental interactions have not been well characterized. We performed RNA profiling of FL and paired tFL samples transformed to DLBCL (n=11 pairs). For RNA profiling, we utilized libraries prepared using RNA isolated from archival FFPE tissues.

  Study EGAS50000000941

• Whole exome sequencing of pre-invasive lung adenocarcinoma in non-smokers

  The prevalence of lung adenocarcinoma (LUAD) has increased sharply in East Asia. Early diagnosis leads to better survival rates, but this requires an improved understanding of the molecular changes during early tumorigenesis, particularly in non-smokers. We performed whole exome-sequencing and RNA-sequencing for samples from 94 East Asian patients with precancerous lesions (25 with atypical adenomatous hyperplasia [AAH]; 69 with adenocarcinoma in situ [AIS]) and 73 patients with early invasive lesions (minimally invasive adenocarcinoma [MIA]). Cellular analysis revealed that the activities of endothelial and stromal cells could categorize tumors into molecular subtypes within pathology-defined lesion types. These subtypes were linked with the advanced radiology-defined lesion type and coordinated with immune cell infiltration throughout the progression of LUAD. Characterization of these lesion types identified positively selected mutation patterns, and suggested that angiogenesis of the late-stage AIS type potentially contributes to tissue invasion of the MIA type. Our findings offer a novel resource that may help to improve early diagnosis and patient prognosis, and also suggest possible approaches for early disease interception.

  Study EGAS50000000438

• Upcycling and merging data to challenge the dogma and identify new therapeutic targets for Glioblastoma

  Glioblastoma is the most common brain tumour. Characterised by a poor prognosis and its recurrence after multimodal treatments, the search for preventable risk factors has been mainly inconclusive up to date. Recently, the data merge from datasetsdeposited at the EGA allowed Aaron Diaz’s team to discover that the glioblastoma cells shift toward a mesenchymal phenotype when the tumour is recurring. Challenges in glioblastoma research As for virtually all cancer types, many efforts have been made to unveil the molecular features responsible of the disease, and several cellular pathways have indeed been identified as being frequently mutated in glioblastoma. Nonetheless, targeted therapies based on identified genes have so far failed to improve outcome, thus survival mostly relies on a standard treatment unchanged since 2005. This is a frustrating situation for both the scientific and medical communities, and above all for the patients, still facing a dreadful path. Some researchers hypothesised that this may be due to the inability to efficiently target cancer stem cells, the originators of the other cell types, thus inducing cancer relapse. In 2019 Charles P. Couturier and colleagues sequenced RNA from single cells of freshly excised glioblastomas of 16 patients, and demonstrated that glioblastoma cells replicate normal brain cell development with a conserved neural cancer cell hierarchy centered around glial progenitor-like cells. In this way, they helped identify the possible target cells to improve efficacy and durability of treatment. Data upcycling at the EGA: the glioblastoma case study Single cell RNA sequencing is generated with a laborious and expensive protocol. The quality of the starting material is crucial (cells sample freshly extracted from the patients) and often several attempts are needed before producing reliable quality sequencing results. Collecting big numbers of patients is also challenging. The sequencing data produced by Kevin Petrecca’s group in Montreal, Canada and deposited at the EGA (EGAS00001004422) was recently upcycled by Lin Wang in San Francisco, California and pooled with their freshly produced ones, and then deposited at the EGA as Dataset EGAS00001004909 The data merge allowed Aaron Diaz’s team to discover that the glioblastoma cells shift toward a mesenchymal phenotype when the tumour is recurring. They profiled 86 primary-recurrent patient-matched paired glioblastoma samples with single-nucleus RNA, among other techniques. These very comprehensive results lead the team to challenge the findings from several other cancer fields where chemotherapy standard chemo-radiation therapy selection pressure at the level of genomic alterations; this is indeed not the case for glioblastoma, where the pressure results in phenotypic transition between cellular states. Several technical controls were made to ensure that the merge of the data was not introducing a bias in the results, and an Inter-table analysis demonstrated nearly equal contribution to overall variance from each of the studies included, indicating that the findings were not due to inter-laboratory technical effect. A novel principal-component analysis showed that the largest contribution to variation in primary glioblastoma neoplastic cells was an axis between MES (mesenchymal) and proneural expression programs. In summary, treating glioblastoma often makes a MES, as commented by Lucy Stead about this remarkable work. Check Aaron Diaz’s team Nature Cancer paper, with strong technical tools, state of the art analysis and data from different sources converging to the same outcomes that made possible a significant step toward a better handling of a frightful cancer. References Couturier, C.P., Ayyadhury, S., Le, P.U. et al. Single-cell RNA-seq reveals that glioblastoma recapitulates a normal neurodevelopmental hierarchy. Nat Commun 11, 3406 (2020). Stead, L.F. Treating glioblastoma often makes a MES. Nat Cancer 3, 1446–1448 (2022). Wang, L., Jung, J., Babikir, H. et al. A single-cell atlas of glioblastoma evolution under therapy reveals cell-intrinsic and cell-extrinsic therapeutic targets. Nat Cancer 3, 1534–1552 (2022). Related links: Kevin Petrecca’s group dataset deposited at the EGA: EGAS00001004422 Lin Wang dataset deposited at the EGA: EGAS00001004909

  Blog upcycling-and-merging-data-to-challenge-glioblastoma

• The last addition to the list of clonal evolution studies in the EGA

  Tumours of different types may arise from a single cell with at least one malignant mutation. Accumulation of subsequent hits generates lineages of cells with different cancer transformation power; thus, every clone follows an autonomous evolution, and several clones may co-exist into the same solid or liquid tumour. Clonal evolution studies are not new: the conceptual power of this approach has been recognized since the early ’80, especially in leukaemia patients. Nonetheless, these studies greatly benefit from recent technological improvements -machineries and protocols-, which enable ever-increasing sequencing depth, allowing the detection of low frequency variants and consequently more tumour clones. Note: the figure belongs to the original paper and is owned by the authors. Researchers at the University of Kyoto in Japan, have indeed used state of the art knowledge to investigate the clonal evolution of chronic myeloid leukemia (CML) in a large panel of patients. CML progresses from a majoritarian benign chronic phase into an often fatal blast crisis phase. It is nowadays known that blast crisis are triggered by genetic instability and disrupted DNA repair, which lead to accumulation of DNA mutations. The use of thyrosine kinase inhibitors targeting the Philadelphia chromosome have enormously improved the treatment of blast crisis, nonetheless resistant patients are still a medical challenge. Yotaro Ochi and collegues used Whole Exome Sequencing of paired chronic phase and blast crisis samples to depict the genetic landscape of progression from the first to the second stage of the disease (as illustrated in their figure). Their results were published in Nature Communication on this May 2021. They used a dataset previously deposited at the EGA (EGAS00001003071) and their new generated data have been made available under accession number EGAS00001005075. Their analysis of the occurring mutations contribute to understanding the mechanisms of CML evolution and therefore develop a protocol for early management of patients with a TKI resistance signature. Up to date, the EGA hosts 99 clonal evolution studies, developed in several cancer types, including myeloma, renal carcinomas and ovarian cancer, in addition to various leukaemia subtypes, where this approach has been more widely applied and produced important results and seminal papers. From a pan-cancer point of view, the definition of tumour architecture through clonal evolution studies is instrumental to unravel the mechanisms of disease progression and treatment-resistance insource. At the EGA, we are honoured to contribute to exciting and important science advances with our service!

  Blog the-last-addition-to-the-list-of-clonal-evolution-studies-in-the-ega

• Reading about genomic analysis of pan-neuroblastoma

  There are virtually no research fields that do not benefit from the large-scale genomic analysis that are so popular nowadays. For research into diseases with highly diverse clinical behavior like pediatric neuroblastoma, nevertheless, this type of approach has been a game-changer. Samuel W. Brady and colleagues, from St. Jude Children’s Research Hospital in Memphis, have gathered data from 702 neuroblastomas which were profiled by whole genome sequencing (WGS, n = 205), whole exome sequencing (WES, n = 539), and/or RNA-Seq. Less than half of the samples were generated during this study, while the others were obtained from previous work. Part of the WES data was indeed deposited in EGA in 2012 (EGA study EGAS00001000213). A scratchy summary about the huge work from Samuel W. Brady and colleagues, published in Nature Communications this October 2020 “These findings suggest that the sympathetic nervous system, the tissue from which neuroblastoma arises, is susceptible to different oncogenic insults at different times during development, which could be explored in future investigations using animal models.” Conclude the authors, highlighting the importance of their findings. Pediatric neuroblastomas are classified according to clinical and molecular features into low, intermediate, and high risk. Age plays a critical role in the classification, with younger children having an overall better prognosis. The samples included in this study belonged to all ages and risk-groups: the aim of such set up is the identification of age-associated molecular aberrations. The authors defined a mutational landscape of pediatric neuroblastoma including rare driver events, impossible to pick with a smaller data set. Interestingly, most driver mutations' frequency was found to be age-related. Those include alterations on MYCN, TERT, PTPRD, and Ras pathway, in addition to the previously reported ATRX. In addition, the large data set allowed the authors to define co-existence relations between the mutations (mutual exclusion or co-occurrence) which is a crucial puzzle piece to unravel the mechanisms driving the pathogenesis. Enjoy this really interesting article, open source on Pubmed.

  Blog genomic-analysis-of-pan-neuroblastoma

• Drug development for pediatric cancers: the importance of patient' s genomic data re-use

  Cancer in children is uncommon and the overall prognosis for most pediatric cancers is good. However, while the combined survival rates have improved over the last decades, certain childhood malignancies, such as high-grade gliomas or metastatic sarcomas, still remain incurable in most patients. New strategies for targeting those devastating diseases are imperative, and patient genomic data can become a key asset in the process [1]. The Pediatric Cancer Genome Project’s datasets At EGA, we store over 500 datasets with sequencing data from pediatric cancer patients. A remarkable case is the datasets belonging to the Pediatric Cancer Genome Project (PCGP) from St. Jude Children’s Research Hospital–Washington University (Table 1 - EGAS list). PCGP is an ambitious effort to identify the mutations that drive childhood cancer and find new cures. Those datasets include 600 patients with complete tumor and normal genomes from 15 different tumor types. PCGP datasets have been an important resource for various studies that pooled genomic data from different public and in-house datasets to perform extensive genomic characterizations and publish comprehensive pan-cancer pediatric studies [2],[3]. Other interesting pediatric cancer datasets are from the Pediatric Brain Tumor Consortium, Sickkids or ICGC PedBrain project. How data reuse impacts drug development for pediatric cancers Recently, in October 2024, Nature Communications published a work led by the University of Michigan where authors used data from EGA (PCGP) check the table below as part of their strategy to pursue the identification of new tumor vulnerabilities susceptible to becoming novel therapeutic targets in diffuse midline glioma. Diffuse midline gliomas (DMG) are treatment-resistant and uniformly fatal pediatric brain tumors. The prognosis of this brainstem tumor is dismal with a median overall survival of 9–12 months from diagnosis. In this study, the authors first developed a lab model of the disease and reanalyzed data from the EGA to identify genes potentially linked to tumor severity. This approach revealed involvement of specific metabolic pathways, suggesting new possibilities for therapeutic intervention. In mice, the study shows that the use of statins can improve survival. For a brief overview of the underlying science, diffuse midline gliomas present intratumor heterogeneity with subpopulations of less-differentiated oligodendrocyte precursors and more differentiated astrocytes. Authors established in vitro models to recapitulate both phenotypes and identify metabolic programs in both subpopulations. To determine the clinical relevance, the authors re-used gene expression data from 76 DMG patients identifying a gene signature predicting decreased overall survival. After extensive metabolic characterization of subpopulations, authors defined strategies to target specific metabolic vulnerabilities. Pre-clinical experiments in mice using OXPHOS inhibitors and statins showed a reduction in tumor burden and increased overall survival [4]. In this scenario, the re-use of high-quality patient’s sequencing data appeared as an advantageous strategy to support the clinical relevance of in-vitro and pre-clinical findings. On the other hand, in rare conditions such as childhood cancer, the availability of public datasets and the possibility of pooling data from different sources can be the only way to obtain impactful results that lead to improvements for patients. References Davidoff, A. M. Pediatric Oncology. Seminars in pediatric surgery 19, 225–233 (2010). Gröbner, S. N. et al. The landscape of genomic alterations across childhood cancers. Nature 555, 321–327 (2018). Venu Thatikonda et al. Comprehensive analysis of mutational signatures reveals distinct patterns and molecular processes across 27 pediatric cancers. Nature Cancer 4, 276–289 (2023). Mbah, N. E. et al. Therapeutic targeting of differentiation-state dependent metabolic vulnerabilities in diffuse midline glioma. Nature Communications 15, (2024). Datasets and Studies ID Title Access Policy Year EGAD00001000134 Driver mutations in histone H3.3 and chromatin remodelling genes in paediatric glioblastoma McGill-DKFZ Pediatric Brain Tumour Consortium 2016 EGAS0000100192 Somatic Histone H3 Mutations in Diffuse Intrinsic Pontine Gliomas and Non-Brainstem Paediatric Glioblastomas St. Jude Children's Research Hospital - Washington University Pediatric Cancer Genome Project 2017 EGAS00001000575 Whole genome sequencing and whole exome sequencing of DIPG tumors and matched normal tissue The hospital for sick children ("SickKids") 2016 EGAD00001000792 Exome sequencing reads of paediatric glioblastoma McGill-DKFZ Pediatric Brain Tumour Consortium 2016 EGAD00001002006 Whole genome sequencing of paediatric glioblastoma in the ICGC PedBrain project ICGC PedBrain project 2016

  Blog drug-development-for-pediatric-cancers

• Highlights from 2025: Our top 10 requested studies

  In 2025, as in previous years, EGA has facilitated the reuse of numerous datasets spanning a broad range of research and healthcare topics. To reflect on this activity, we would like to highlight the top 10 studies behind the most requested datasets of last year. Notably, the vast majority of these highly requested datasets are cancer-focused, particularly involving the study of immune checkpoint inhibitor therapies. Immune checkpoint inhibitors (ICIs) have become one of the most transformative advances in modern cancer therapy. These monoclonal antibodies enhance antitumor immune responses by targeting inhibitory immune checkpoint molecules. While ICIs have led to remarkably favorable and durable responses in some patients, treatment outcomes vary widely. This variability has fueled intense research efforts aimed at understanding resistance mechanisms, exploring combination therapies, and discovering predictive biomarkers to better stratify patients and guide treatment decisions. Study Title Disease 1 EGAS00001005503 Integrated genomic analyses reveal molecular correlates of clinical response and resistance to atezolizumab in combination with bevacizumab in advanced hepatocellular carcinoma Liver Cancer 2 EGAS00001005013 Intratumoral plasma cells predict outcomes to PD-L1 blockade in non-small cell lung cancer Lung Cancer 3 EGAS00001004809 BIOKEY: A single-cell catalogue of the dynamic changes underlying Checkpoint Immunotherapy response in Early Breast Cancer Breast Cancer 4 EGAS50000000138 BIOKEY: Immune heterogeneity in small cell lung cancer and vulnerability to immune checkpoint blockade Lung Cancer 5 EGAS50000000497 Bladder cancer subtyping study across 4 atezo clinical trials Bladder Cancer 6 EGAS50000000689 MOSAIC - Multi-Omics Spatial Atlas In Cancer Cancer Atlas 7 EGAS00000000083 METABRIC (Breast cancer - genome and transcriptome) Breast Cancer 8 EGAS00001005702 Human genomic and phenotypic synthetic data for the study of rare diseases Synthetic data 9 EGAS00001002556 TGF-β attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells Bladder Cancer 10 EGAS00001004353 Molecular subsets in renal cancer determine outcome to checkpoint and angiogenesis blockade Renal Cancer The most requested dataset of the year was EGAD00001008128. It belongs to a study that analyzed 358 liver cancer patients enrolled in clinical trials and treated with the combination of atezolizumab (anti–PD-L1) and bevacizumab (anti-VEGF), atezolizumab alone, or sorafenib. The study highlights how bevacizumab enhances anti-PD-L1 effect and identifies candidate biomarkers to predict treatment response. Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and the third leading cause of cancer-related death worldwide [1]. Because symptoms often appear late, many patients are diagnosed at advanced stages, when curative options are no longer viable. For many years, sorafenib—a multikinase inhibitor approved in 2007—was the only systemic treatment available. Although numerous clinical trials followed, it was not until a decade later that additional therapies showed meaningful clinical benefit. Among these advances, immunotherapy emerged as a promising first-line option [2]. In particular, the combination of atezolizumab and bevacizumab demonstrated improved survival in patients with advanced HCC, reshaping the treatment landscape. However, treatment efficacy remains limited by the complex and diverse hepatic microenvironments that influence immune responses, as well as by high intratumoral heterogeneity. Identifying robust predictors of treatment response is critical to enable effective patient stratification and maximize the benefits of these therapies [2]. The continued demand for these datasets highlights the interest and critical role of data reuse in deepening our understanding of cancer, accelerating the development of more effective therapies, and improving patient outcomes. By providing secure access to high-quality genomic and clinical data, EGA acts as a catalyst for biomedical progress. References Sung, Hyuna, et al. "Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries."CA: a cancer journal for clinicians 71.3 (2021): 209-249. Ladd, Alexandra D, et al.“Mechanisms of drug resistance in HCC.” Hepatology 79(4) (2024): 926-940

  Blog top-10-studies-2025

• Iberian Roma Data Access Commitee

  Dac EGAC00001002958

• Oslo University Hospital Data Access Committee for REMNANT study datasets submitted to FEGA Norway

  This is the Data Access Committee appointed by Oslo University Hospital for controlled access to datasets from the REMNANT study under the REK approval no 66696. REMNANT - RElapse from Mrd Negativity As iNdication for Treatment, where relapse from myeloma is studied.

  Dac EGAC50000001121

• Gut microbiota biomarkers associated with Multiple Myeloma: The REMNANT Study

  We aimed to investigate the association between gut microbiota composition and multiple myeloma–related clinical parameters, including treatment response, clinical outcomes, and treatment-related adverse effects

  Study EGAS50000002142

• scRNAseq data from HNSCC biopsies before and after BA treatment

  Tumor biopsies from 6 head and neck small cell carcinoma (HNSCC) berfore and 3-weeks after bintrafusp-alfa (BA) treatment were analyzed using single-cell RNA sequencing.

  Study EGAS50000002114

• CIML Lymphoma Single-Cell Atlas Project

  Data Accession Committee for evaluating requests for accessing the sequence-level data produced by CIML investigators in the frame of the Lymphoma Single-Cell Atlas Project.

  Dac EGAC50000001133

• Norwegian Sarcoma Consortium Data Access Committee for datasets submitted to FEGA Norway

  This is the Data Access Committee appointed by Oslo University Hospital for the Norwegian Sarcoma Consortium datasets submitted to FEGA Norway, under the Regional Ethical Approval 944504.

  Dac EGAC50000001153

• NoSarC -- Norwegian Sarcoma Consortium. Towards individualized therapy for orphan cancer.

  This study is initiated and conducted by the Norwegian Sarcoma Consortium - a collaboration between 5 Norwegian hospitals and universities, coordinated by Oslo University Hospital. The NoSarC project was initiated in 2014 and aimed to perform molecular characterization of samples from a national, population-based cohort of sarcoma patients, to identify tumors which are sensitive to therapies already developed and approved for more common cancers. The NoSarC cohort in this study consists of Tumor-Normal pairs from 287 subjects that has been Whole Exome Sequenced.

  Study EGAS50000002163

• Cell-Free DNA Genomic and Fragmentomic Features for Early Outcome Prediction in Diffuse Large B-Cell Lymphoma

  Purpose: Diffuse large B-cell lymphoma (DLBCL) patients need an accurate and early risk stratification strategy as prompt therapy escalation may improve outcome. Patients and methods: We evaluated cell free DNA (cfDNA) genomic and fragmentomic features in 190 patients with large B-cell lymphoma from more than 40 hospitals in the Benelux using Whole Genome Sequencing (WGS). We defined a new metric called ACT score (Aberrations, Contribution of short fragments, Terminal motif analyses) by combining four cfDNA signals and evaluated its performance to predict end-of-treatment (EOT) response and survival. Results: Individual cfDNA features and ACT score were altered per EOT response after one cycle of treatment (T1). The ACT score at T1 outperformed all individual cfDNA features in predicting EOT response (AUC = 0.74). Patients with a positive ACT score had an inferior outcome compared to ACT score negative patients [progression-free survival (PFS): HR 5.0 (95% CI 3.1-8.2), log-rank test, p < 0.0001) and overall survival (OS): HR 6.5 (95% CI 3.5-12.2), log-rank test, p < 0.0001]. The 2-year PFS in ACT score positive and negative patients were 29.8% and 78.1%, respectively. The prognostic value of the ACT score is independent of the International Prognostic Index and interim imaging. Conclusions: ACT score computed from a single plasma sample collected after one cycle of treatment can predict clinical outcome. This low-cost and easy-to-interpret test does not require tissue biopsies or a priori knowledge of mutations and has the potential to aid patient selection in interventional clinical trials and risk-adapted treatment strategies.

  Study EGAS50000000412

• SCANDARE HNSCC

  Prospective biobanking study in SCANDARE HNSCC (Head and Neck Squamous Cell Carcinoma) patients aiming at better understand the link between the molecular alterations of the tumor itself, its microenvironment and immune response.

  Study EGAS50000001158

• Orthotopic Patient-Derived Organoid Model Reveals Tumor Microenvironment-Dependent Phenotypes in Lung Squamous Cell Carcinoma-WTS

  RNAseq data

  Study EGAS50000002025

• Orthotopic Patient-Derived Organoid Model Reveals Tumor Microenvironment-Dependent Phenotypes in Lung Squamous Cell Carcinoma-WGS

  WGS

  Study EGAS50000002026

• Orthotopic Patient-Derived Organoid Model Reveals Tumor Microenvironment-Dependent Phenotypes in Lung Squamous Cell Carcinoma-WES

  WES data

  Study EGAS50000002027

• Distinct molecular profiles characterize the spontaneous growth rate of IDHmt low-grade astrocytomas and oligodendrogliomas

  The life expectancy of patients with diffuse IDH-mutant low-grade gliomas (IDHmt LGG) ranges from 5 to over 20 years. Tumor behavior, including spontaneous growth rate, varies even within homogeneously classified subtypes of oligodendroglioma and astrocytoma. Risk-adjusted treatment strategies are needed to avoid therapy-related toxicities without compromising outcome. The spontaneous tumor volume growth rate (TVGR) serves as a prognostic marker and predicts response to therapy. Accurate prediction of TVGR through biomarkers would enable improved evidence-based risk management.

  Study EGAS50000002072

• Expansion and cytotoxicity of mucosal T cells in enterocolitis associated with BCMA CAR-T cell therapy

  This study investigates immune effector cell enterocolitis (IEC-EC) associated with ciltacabtagene autoleucel (cilta-cel), a B-cell maturation antigen (BCMA)-targeting chimeric antigen receptor (CAR) T-cell therapy for relapsed or refractory multiple myeloma. IEC-EC was characterized by infiltration and expansion of clonal CAR-positive cytotoxic T-cell populations within the gastrointestinal tract. Molecular analyses were performed to characterize CAR T-cell clonality, persistence, vector integration, and genomic alterations associated with this complication. This dataset contains whole-genome sequencing (WGS) and whole-exome sequencing (WES) FASTQ files generated as part of the study and associated with the corresponding manuscript.

  Study EGAS50000002146

• Additional medulloblastoma cfDNA low-coverage WGS

  Low-coverage whole-genome sequencing of cell-free DNA from CSF samples collected across timepoints from medulloblastoma patients enrolled on SJYC07 and SJMB03. These data supplement the previously deposited cohort (EGAS00001005592).

  Study EGAS50000002128

• Germline whole-genome sequencing (WGS) of colorectal cancer patients

  Whole-genome sequencing (WGS) data generated from matched germline blood DNA samples obtained from patients with primary or metastatic colorectal adenocarcinoma (n=37). These data were generated as part of a multiomic study integrating whole-genome sequencing with single-cell multiome profiling to investigate recurrent epigenetic alterations in colorectal cancer evolution and the tumour microenvironment.

  Dataset EGAD50000002942

• microRNA and isomiR profiling of Stage 1 epithelial ovarian carcinoma

  Study EGAS00001006617

• Nanopore whole-genome sequencing of human sarcomas

  We performed single and multi-region nanopore whole-genome sequencing on human sarcoma samples from adult and paediatric patients.

  Study EGAS50000000651

• Bladder cancer subtyping study across 4 atezo clinical trials

  Checkpoint inhibitors targeting the PD-1/PD-L1 interaction have revolutionized cancer therapy across many indications. In urothelial carcinoma (UC), several PD-1/PD-L1 inhibitors have been approved for treatment in localized and metastatic disease. While these agents provide durable clinical benefit in a number of patients, the majority do not respond. A better understanding of the molecular mechanisms underlying response and resistance is needed to tailor existing treatments and develop new therapeutics in UC. We profiled tumors from 2803 patients from four late-stage randomized clinical trials evaluating the PD-L1 inhibitor atezolizumab in metastatic UC and muscle-invasive bladder cancer by RNA-seq, a targeted DNA panel, immunohistochemistry, and digital pathology. A machine learning algorithm identified four UC transcriptional subtypes, representing luminal desert, stromal, immune, and basal tumors. Overall survival benefit from atezolizumab over standard-of-care was observed in immune and basal tumors (45% of patients). Digital pathology showed enrichment of human interpretable features in specific molecular subtypes, and a self-supervised approach could classify molecular subtypes from H&E slides with high accuracy, suggesting that machine-based image interpretation could accelerate tumor molecular profiling. Somatic alterations showed limited capacity to identify responders. Finally, mechanisms of benefit with PD-L1 blockade appeared different in immune and basal tumors, with immune tumors benefiting from B/plasma cell enrichment, while basal tumors benefited from granulocyte enrichment. This study represents the largest integration of UC molecular and curated clinical data in randomized clinical trials to date, paving the way for prospective clinical studies tailoring treatment regimens to specific tumor molecular subtypes in UC and other indications.

  Study EGAS50000000497

• Nanopore Amplicon Sequencing data of 33 patient tumor and one primary cell line samples - all uveal melanoma

  Nanopore Amplicon Sequencing data of 4 amplicons in the GNA11, GNAQ, SF3B1 and SRSF2 genes in 33 uveal melanoma patient tumor samples and one primary cell line sample. Sequencing was performed on a MinION platform (Mk1B) using the SQK-RBK114.24 kit multiplexing 24 samples per flow cell. Basecalling was performed using the dna_r10.4.1_e8.2_400bps_sup@v4.3.0 model and dorado v.0.6.2.

  Study EGAS50000002137

• Cell-free DNA methylation profiling in classic Hodgkin lymphoma using cfEM-seq

  Genome-wide DNA methylation profiling using cfEM-seq and Ultima Genomics sequencing in classic Hodgkin lymphoma. The study includes plasma-derived cell-free DNA from patients with classic Hodgkin lymphoma together with classic Hodgkin lymphoma and anaplastic large cell lymphoma cell lines. The deposited data comprise original sequencing CRAM files and associated sequencing-provider QC metadata.

  Study EGAS50000002038

• Single-cell characterization of anti-LAG3+anti-PD1 treatment in melanoma patients

  Study EGAS00001005580

• Orthotopic Patient-Derived Organoid Model Reveals Tumor Microenvironment-Dependent Phenotypes in Lung Squamous Cell Carcinoma

  This dataset contain NGS raw fastq data from 27 samples, including 5 WES, 4 WGS, 1 shallow WGS(sWGS) and 17 RNAseq.

  Dataset EGAD50000002915

• Multi-modal single-cell sequencing dataset for large B cell lymphoma samples

  This dataset contains raw FASTQ sequencing data from single-cell transcriptome, BCR, TCR, and HTO profiling of 72 hashtag-multiplexed large B-cell lymphoma samples, generated using the 10x Genomics Chromium Single Cell 5′ workflow and sequenced on an Illumina NextSeq 2000.

  Dataset EGAD50000002966

• scRNA-seq data from HNSCC samples before and after BA treatment

  Tumor biopsies from 6 head and neck small cell carcinoma (HNSCC) berfore and 3-weeks after bintrafusp-alfa treatment were analyzed using single-cell RNA sequencing (droplet-based 3' 10x genomics). Library were sequenced using the Chromium plateform. Generated fastq files were processed using cellranger to generat the count matrices.

  Dataset EGAD50000003043

• Low-coverage whole-genome sequencing

  Low-coverage whole-genome sequencing from CSF samples collected across timepoints from medulloblastoma patients

  Dataset EGAD50000003058

• Gut microbiota biomarkers associated with Multiple Myeloma: The REMNANT Study

  This dataset contains longitudinal shotgun metagenomic sequencing of stool samples collected across two time points from patients with newly diagnosed multiple myeloma (153 patients , 306 samples total). The suffix "scr" denotes the screening time point, when patients were enrolled in the study and samples were collected before initiation of treatment. Following inclusion, patients received standard multiple myeloma treatment according to Norwegian clinical guidelines. The treatment consisted of four cycles of induction therapy with bortezomib, lenalidomide, and dexamethasone (VRd), followed by autologous stem cell transplantation (ASCT) for eligible patients, and subsequently four cycles of consolidation therapy with VRd. The second stool sample was collected after completion of consolidation therapy. Therefore, samples with the suffix "post_con" represent the post-consolidation time point. This is the time point which MRD was measured. Samples were sequenced on the Illumina NovaSeq X platform. Raw data consists of paired FASTQ files per sample.

  Dataset EGAD50000003072

• Norwegian Sarcoma Consortium Tumor-Normal pairs Whole Exome Sequencing dataset 2026

  The dataset consist of Whole Exome Sequencing results from 287 sarcoma patients in the NoSarC cohort, from which matched sarcoma tumor samples and normal blood samples where pairwise analysed together using the Dragen v.3.7 analysis framework. The dataset contains fastq files as used for input to the analysis and the Dragen 3.7 vcfs outputs for germline mutations, somatic mutations and copy number variants (and their corresponding index files). The phenotype information provided includes: age, sex, times of birth, surgery, death, last followup or last known alive, local recurrence assessment, metastatic-free survival assessment and reccurence-free survival assessment. All times provided in a month-year format. 8 disease and topography fields such as disease status at inclusion, time of WES tumor sampling and final histological diagnosis, as well as tumor lesion sampled and localization of the sarcoma in final diagnosis, are inluced, as well as 5 outcome-related variables incl overall survival and cause of death. Finally 5 treatment-related variables are included. Due to high sequencing depth in several samples, the provided fastq files is the result of a screening process and downsampling if nessearcy, done before the Dragen analysis. The procedure is documented in the included README_fastq_downsampling.txt

  Dataset EGAD50000003102

• Carcinoma of Unknown Primary (CUP): A comparison across tissue and liquid biomarkers (CUP-COMP) study

  The Carcinoma of Unknown Primary (CUP): A comparison across tissue and liquid biomarkers (CUP-COMP) study (NCT: NCT04750109) was sponsored by The Christie NHS Foundation Trust and funded by Innovate UK.

  Study EGAS50000001821

• A single-cell multi-omic atlas of nodal B-cell non-Hodgkin lymphomas

  Nodal B-cell non-Hodgkin lymphomas (B-NHL) are a clinically diverse set of cancers thought to originate from distinct stages of B-cell maturation. To characterize intratumor heterogeneity across B-NHL entities, we performed CITE-Seq (cellular indexing of transcriptomes and epitopes) on 51 lymph node samples including mantle cell lymphoma (MCL, n = 8), follicular lymphoma (FL, n = 12), germinal center (GCB, n = 5) or activated B-cell (non-GCB/ABC, n = 7) diffuse large B-cell lymphoma (DLBCL) and marginal zone lymphoma (MZL, n = 11) tumors, in addition to non-malignant reactive lymph nodes (rLN, n = 8). We also performed 5 prime scRNA-seq with immune receptor profiling for 11 of these samples (2 rLN, 2 MCL, 3 FL, 2 DLBCL, and 2 MZL).

  Study EGAS50000000342

• Genomic correlates with survival after treatment of oesophageal adenocarcinoma by surgery alone

  A 451 cancer-associated gene panel, described in McCabe et al. 2019, was used to assess genomic alterations associated with survival in a Swedish cohort of 65 oesophageal adenocarcinoma (OAC) patients who underwent surgical resection without perioperative treatment. Primary tumour tissues and matched normal squamous oesophagus tissues were collected from each individual. Laboratory studies were performed at the Centre for Applied Medical Research (AMR) and sequencing was performed at the Kinghorn Cancer Centre. 64 of 65 cancer samples and 61 matched squamous oesophageal mucosa control tissues were successfully sequenced and passed minimal quality metrics.

  Study EGAS50000002190

• cfEM-seq methylation sequencing in classic Hodgkin lymphoma

  Reference-aligned cfEM-seq sequencing data generated on the Ultima Genomics platform from classic Hodgkin lymphoma plasma samples and lymphoma cell lines, together with associated index and provider QC metadata.

  Dataset EGAD50000002929

• Nanopore Amplicon Sequencing of 34 uveal melanoma samples (33 patient tumors + 1 primary cell line) in 4 mutational hotspot regions for genotyping

  Sequencing of one mutational hotspot region in each of the genes GNA11, GNAQ, SF3B1 and SRSF2. 33 patient tumor samples and a primary cell line were analyzed, all uveal melanoma. Amplicons were generated in a PCR step followed by AMPure XP clean up. Amplicons were pooled equally per sample and sequenced for genotyping.

  Dataset EGAD50000003067

• WES dataset of mesothelioma CONFIRM study

  This dataset comprises 33 whole-exome sequencing (WES) samples from mesothelioma patients, including 18 tumor, 13 matched normal, and 2 relapsed samples.

  Dataset EGAD50000002621

• RNA sequencing dataset of mesothelioma CONFIRM study

  This dataset includes 27 RNA sequencing tumors from mesothelioma, of which two are derived from relapsed patients.

  Dataset EGAD50000002622

• How to use the EGA search box

  Tips on how to search Tip 1: Use simple and clear keywords No matter what you are searching: a study, a dataset or a committee; the search will look recursively in all the fields of all the entities in the database. Thus, there is no need to specify which type of data you are looking for. Try to use single and specific words and avoid long and complex expressions. Try it out: cancer rna-seq instead of cancer studies using RNA-seq. Tip 2: Narrow your results using search operators Search operators give you more control over the results. They manage the way keywords are used to restrict the search. By default, the search engine applies an AND operator when a space is inserted between two words. Include OR (capitalised) between the keywords in your search in order to show pages containing either term or both terms. Try it out: Search all the studies based on transcriptomic data, including those ones that use RNA sequencing to obtain this data. Tip 3: Do not worry about details The search engine automatically checks for the most common spelling mistakes of given words and suggests the use of the correct version. It also ignores capital letters and most punctuation so none of these mistakes will hamper the accuracy of your search. Besides, when using a combination of words, the search engine will suggest similar combinations with a higher number of results. Tip 4: Use words that are likely to appear within the text Unfortunately, our search engine is not able to interpret words yet. Therefore, words which do not appear in the body text will not return any result despite them denoting a group of present terms. For instance, if we are looking for studies focused on neurological diseases, using neurological as a keyword would not be the best option. Instead, you should use the name of each neurological disease such as bipolar disorder, schizophrenia or depression. In the same fashion, incomplete words do not return any result. Please include an asterisk (*) before and/or after the incomplete word in order to allow for partial matching. Try it out: canc* instead of canc(without wildcard). Also you can try out *oma, or even *geneti* Tip 5: Narrow down your search by filtering the results You can filter the results by the following options: general, study, and dataset. Under these categories, you will find more sub-filters to refine your search. Make sure that you click on Apply in order to filter the results. You can clear the filtering anytime. Advanced Searches Additional example queries: Search for DUO:0000005 and word lung example query Or write: DUO:0000005 AND lung Search for an exome sequencing study in dbGaP example query Or write: study_type: "exome sequencing" AND repository:dbGaP Search for an exome sequencing study in EGA example query Or write: study_type: "exome sequencing" AND repository:ega Search for a dataset that is at the EGA beacon and with type "Whole genome sequencing" and with number of samples between 100 and 2000 and with technology "Illumina HiSeq 2000" example query Or write: is_in_beacon:true AND dataset_types:"Whole genome sequencing" AND samples:[100 TO 2000] AND dataset_technologies:"Illumina HiSeq 2000" Search for PMIDS example query Or write: pmids:32025007

  Documentation discovery/metadata/search-box

• Cell-free DNA Whole Genome Sequencing for Non-Invasive Minimal Residual Disease Detection in Multiple Myeloma

  Minimal residual disease (MRD) monitoring in multiple myeloma (MM) relies on invasive bone marrow (BM) biopsies, which often yield insufficient tumor material. We performed whole genome sequencing of cell-free DNA from 163 plasma samples from 51 patients to develop a non-invasive MRD classifier. BM WGS identified a median of 2,502 clonal mutations, enabling cfDNA tracking at levels comparable to BM-based MRD testing. The cfDNA classifier achieved a mean AUC of 0.86 against multiparameter flow cytometry and targeted immunoglobulin sequencing (clonoSEQ), and MRD negativity after one year of maintenance was strongly associated with two-year relapse-free survival probability (Hazard Ratio = 24), with cfDNA changes preceding clinical progression by a median of 12.6 months. To establish a BM-agnostic mode, a plasma-only classifier trained on baseline cfDNA established a mean AUC of 0.79 compared to BM clinical testing and stratified relapse risk (HR = 4.18), enabling MRD detection in patients with suboptimal BM samples. Longitudinal cfDNA profiles detected subclonal evolution in half of profiled patients. Cell-free DNA whole genome sequencing provides a sensitive, scalable, and clinically informative platform for non-invasive MRD monitoring in MM.

  Study EGAS50000002055

• Visium Spatial transcriptomic profiling of IOKIN samples

  This study aimed to define the spatial tumor microenvironment features associated with response to immune checkpoint blockade in recurrent/metastatic head and neck squamous cell carcinoma (RM-HNSCC). Spatial transcriptomic profiling was performed on tumor biopsies from ICB-naive patients to characterize malignant, stromal, and immune cell states and their spatial interactions. The study investigated how intratumoral heterogeneity, cancer-associated fibroblast programs, immune contexture, and tumor-microenvironment signaling relate to subsequent clinical response and progression-free survival following immunotherapy.

  Study EGAS50000002196

• Whole-Exome Sequencing of Tumor Biopsies from a Colorectal Cancer Patient with Two Distinct Primary Tumors and Matched Metastases

  Colorectal cancer (CRC) is a heterogeneous disease in which rare genetic alterations, such as neurotrophic receptor tyrosine kinase (NTRK) fusions, have emerged as clinically relevant therapeutic targets. Here, we report the case of a 69-year-old man with an advanced colorectal carcinoma comprising two intermixed but histologically distinct components: a predominant (>90%) poorly differentiated solid carcinoma and a conventional adenocarcinoma with mucinous features. Whole-exome sequencing (WES) of microdissected tumor regions (two primary tumors and two matched metastases) along with germline DNA analysis revealed profound genetic divergence between the two tumor components. This case illustrates the coexistence of two genetically independent yet spatially inseparable CRCs following distinct evolutionary trajectories—one driven by an NTRK1 fusion and microsatellite instability (MSI), and the other by canonical KRAS and SMAD4 alterations.

  Study EGAS50000001385

• Single-cell study of 14 childhood medulloblastoma patients

  Study EGAS00001006392

• Genomic correlates with survival after treatment of oesophageal adenocarcinoma by surgery alone

  A 451 cancer-associated gene panel, described in McCabe et al. 2019, was used to assess genomic alterations associated with survival in a Swedish cohort of 65 oesophageal adenocarcinoma (OAC) patients who underwent surgical resection without perioperative treatment. This DAC controls access to sequencing data.

  Dac EGAC50000001177

• Multi-omics analysis revealed clonal evolution of the sarcoma patient-derived xenograft

  We performed whole-exome sequencing and RNA-seq of sarcoma PDXs, along with their matched original tumors when available.

  Study EGAS50000001860

• Genomic sequencing in an oesophageal adenocarcinoma cohort

  Genome sequencing was used to assess genomic alterations associated with survival in a Swedish cohort of 65 oesophageal adenocarcinoma (OAC) patients. Primary tumour tissues and matched normal squamous oesophagus tissues were sequenced using a 451 cancer-associated gene panel. This dataset consists of sequence data in fastq format.

  Dataset EGAD50000003139